This paper presents a multiagent system that use an autonomous deliberative case-based reasoningagent to design an efficient working day. The system has been developed to plan and distribute tasks in a health care scenario, specifically in geriatric residences. This model generates a planning of tasks, minimizing the resources necessary for its accomplishment and obtaining the maximum benefits. For this purpose, the queuing theory and genetic algorithms have been include in a CBRarchitecture to obtain an efficient distribution. To evaluate the model, the obtained results have been compared with a previous method of planning based on neural networks

Luis Reboredo, Ana de

Paz Santana, Juan Francisco de

Zato Domínguez, Davinia Carolina

De Paz, Juan F.

Gestion del Repositorio Documental de la Universidad de Salamanca

J.M. Corchado et al. (Eds.): Trends in PAAMS, AISC 90, pp. 51–58. springerlink.com              © Springer-Verlag Berlin Heidelberg 2011 Dynamic Distribution of Tasks in Health-Care Scenarios Carolina Zato, Ana de Luis, and Juan F. De Paz* Abstract. This paper presents a multiagent system that use an autonomous delib-erative case-based reasoningagent to design an efficient working day. The system has been developed to plan and distribute tasks in a health care scenario, specifi-cally in geriatric residences. This model generates a planning of tasks, minimizing the resources necessary for its accomplishment and obtaining the maximum bene-fits.For this purpose, the queuing theory and genetic algorithms have been include in a CBRarchitecture to obtain an efficient distribution. To evaluate the model, the obtained results have been compared with a previous method of planning based on neural networks. Keywords: multiagent systems, queuing theory, genetic algorithm, task schedul-ing, health-care. 1   Introduction During the last three decades the number of Europeans over 60 years old has risen by about 50%. Today they represent more than 25% of the population and it is es-timated that in 20 years this percentage will rise to one third of the population, meaning 100 millions of citizens [1]. This situation is not exclusive to Europe, since studies in other parts of the world show similar tendencies. In the United States of America, people over 65 years old are the fastest growing segment of the population and it is expected that in 2020 they will represent about 1 of 6 citizens totalling 69 million by 2030. Furthermore, over 20% of people over 85 years old have a limited capacity for independent living, requiring continuous monitoring and daily care, for this reason it is important to create a task planner and control system for elderly people.                                                           Carolina Zato . Ana de Luis . Juan F. De Paz Department of Computer Science and Automation, University of Salamanca Plaza de la Merced, s/n, 37008, Salamanca, Spain e-mail: {carol_zato,adeluis,fcofds}@usal.es 52 C. Zato, A. de Luis, and J.F. De Paz Nowadays, a great amount of dynamicproblems can be considered as planning problems. In this kind of problems the aim is to obtain a plan, as well as obtaining a scheduling of the actions along the time, assigning the necessary resources and taking into account restrictions like the benefits. Practical planning has been ap-plied in many different fields and has been combined with different artificial intel-ligence techniques [2] [10]. One of the main challenges has been the development of intelligent systems focused on the improvement of health care sciences that can be applied in hospitals, residences, etc. [3]. The system presented in this paper includes deliberative agents using a case-based reasoning (CBR) architecture, as a way to implement adaptive systems to improve assistance and health care support for elderly and people with disabilities. Agents in this context must be able to respond to events, take the initiative accord-ing to their goals, communicate with other agents, interact with users, and make use of past experiences to find the best plans to achieve goals.The planning model is performed by applying CBR systems [5]. The CBR system integrates into vari-ous stages of reasoning, techniques to estimate resources based on queuing theory and planning using genetic algorithms. The planner has been included into the ALZ-MAS multi-agent system [4][7]. ALZ-MAS generates the distribution of tasks among the personal taking into account their abilities and allowing the dynamic distribution of tasks according to their pro-files.ALZ-MAS facilitates carrying out tasks in a ubiquitous way, such as plans gen-eration for nurse’s and doctors working days, or patient and task assignation. These tasks are executed in a distributed way inside the intelligent environment and are inde-pendent of the location of the processing devices due to the use of mobile devices and wireless networks. The previous planner of ALZ-MAS calculated the distribution of tasks using neural networks to establish the final sequence of actions. Now, this model has been improved using the queuing theory and genetic algorithms. This article is divided as follows: section two presents the proposed model and section three describes the results obtained and the conclusions. 2   Proposed Model The model proposed in this paper focuses on developing a planning mechanism to coordinate the agents. The roles of the agents are: 1.   Patient role. It manages the patient’s personal data and behaviour (monitoring, location, daily tasks, and anomalies). Every hour validates the patient location, monitors the patient state and sends a copy of its memory base to the manager agent in order to maintain backups.  2.   Personnel role. It represents the different workers. This role can have different specializations according to the profile of the workers and his capabilities. In this case, the specialized profiles are doctors, nurses and nurse assistants. 3.   Planner role. Design the overall plan to be implemented by the organization. Sets the number of personnel agents and makes the distribution of tasks de-pending on the specialized role they play. Replan depending on the size of the input queue or inability to accomplishwith a plan. Dynamic Distribution of Tas 4.   Distributor role. Distand checks that each plan. 5.   Manager role. This alarms, etc.) and manincidences, etc.). Fig. 1 Outline of proposed mIn the figure 1 the diffamong them are representthat store the activities tothe image, the planner ageTo carry out the plannBDI. The first point in the(1). /{tC i=Where ti represents the tallocalization (x, y, floor),the agent that performed ting is performed as follow Recovery. During this statrieved. The most similaras the tasks that are currenks in Health-Care Scenarios 5ributing tasks according to its completion by the agentask is being processed within time limits to serve thagent controls the security (patients location, lockages all the information of the system (record databas odel erent agents of the system and some of the interactioned. In the upper corner of the figure appears the task li carry out in the multi-agent system and in the centre ont that generates the planning. ing process, the agent follows the planning model CBR definition of a CBR-BDI model is the definition of cas}...1),,,,,,,( niadpflidtidsti ==  (sk i, ids is the identifier of the task type, idtthe proces f date of entry, p deadline date, d duration, a the ID ohe task. The application of the different stages of reasons: ge the most similar cases to the current case cn+1 are r cases are those containing more tasks of the same typtly queued in the system. The number of cases retrieve3ts e s, e, s st f -e 1)s, f -e-e d 54 C. Zato, A. de Luis, and J.F. De Paz is predefined to subsequently implement the adaptation phase. The set of recov-ered cases are called CCr ⊆ .  Adaptation. During this stage, the recovered information Cr is adapted from cases of memory to generate the entire plan corresponding to the case cn+1. The infor-mation recovered is adapted by applying queuing theory and genetic algorithms. On one hand, the recovered information is used to determine the arrival rate and service for each of the tasks and thus using queuing theory we can determine the number of agents needed to run the indicated tasks. Recovered cases are the basis for constructing initial chromosomes in genetic algorithms.  Review. The review stage is performed automatically as the agents are finalizing tasks. The agent updates the duration of the tasks as they are completed and in turn, makes new plans if any notice is received from the processor agents on the inability to complete a plan under time constraints provided.  Learning. The learning phase is limited to store the case when the day is finished. The new case memory C 'is defined as follows: 1' +∪= ncCC  To limit the size of cases of memory, some are removed from memory if they exceeded a prede-fined age. 2.1   Dynamic Planning Roles The number of agents that should be available in the system is estimated dynami-cally. It is intended that the number of agents suits demand to ensure that the sys-tem utilization factor ρ  is less than 1. This estimate will be done through the use of queuing theory in a model M/G/s, where the arrival rate follows a Poisson dis-tribution (the most commonly used in similar work), the exponential service and the existence of multiple servers (agents). The problem of planning multiple tasks can be reduced to the case of planning for a single task of each type. Thus, for each task a planning is performed inde-pendently so as to calculate the average waiting time and average queue length in-dependently. The average waiting time and the overall average length is reduced to calculating the average values calculated for each of the tasks. In the case of the M/G/s model where s = 1,2,3, ... is the number of agents and given an arrival rate nλ = λ = cte, the service rate when there are n processes is defined by the follow-ing equation (2) [8] [5]. ≥−==snssnnn μμμ1, ..., 2, 1 (2)Where μ represents the average service rate for s available agents. This value de-pends on both the agents and the machine found. Dynamic Distribution of Tasks in Health-Care Scenarios 55 Assuming that the system is in a stable condition, i.e. it meets the utilization factor 1)/( <= sμλρ , the queuing theory [8] allows to calculate the probability that n tasks exists (Pn) in the system, the number of tasks (L) in the system. To determine the optimal number of agents,an estimation that minimizes the cost function is calculated and it depends on the number of agents used and on the waiting time in the queue. The function is defined in a particular way for each ser-vice depending on the actual costs of each agent in the system, though the follow-ing benefit function is provided (4).  ),,',(),,',,..,,( 10 skbpLfbpPPLf bs ⋅−=− μμ  (3) )1('L si)1('L si)1()'/(),,',(−⋅⋅≤⋅−⋅⋅>⋅−⋅⋅=ρρρμspubLspusbupbpLfb (4)Where k is a constant associated with the cost of having an agent working, b the average benefit of performing the task 'μ  is the average time to complete the task obtained from the service rate, p  the average time to execute a task. If the condi-tions of stability are overpass, fb is counted only up to the utilization factor 1. The utilization factor ρ  varies according to the new services added to the queue till it reaches the utilization factor of 1. Following the cost function given in (4), a global cost function is introduced (5) that takes into account the implementation of the various services. ==kjik ffff11 ),...,(  (5)Where if is calculated from equation (4). Because sometimes you might not be given the stability conditions, it is necessary to calculate the terms in order of benefit depending on the type of the task so that when you reach the utilization factor of 100%, the process ends calculating the summation terms. Once the opti-mization function defined in (5) the maximum value is calculated iteratively start-ing with number of agents equal to 1, the fixed value is the first local maximum that corresponds to the global maximum. 2.2   Task Assignment Once the number of starting agents is considered to minimize costs, an allocation of tasks between the available agents has to be done. The distribution of tasks among agents is performed so as to ensure as far as possible that it can perform as-signed tasks in case of delays or the time to perform a task increases. It is per-formed so as to maximize the following function (6): 56 C. Zato, A. de Luis, and J.F. De Paz =−+±kii xx1|1|logmin    (6)Where |1|log xxi −+  takes the sign of the expression xxi − ,iiii catx −−= −1  with it  the maximum time for completion of the task i, 1−ia  the accumulative time to perform the tasks i-1 above and finally ic the time to run the task iplus the time of movement from the task i-1calculated by Floyd algo-rithm [11], which is customized according to the agent selected and calculated from the average value of previously executed tasks. Minimizing the differences get all the tasks to have a uniform distribution of the remaining time so it gets eas-ier to achieve them. The chromosome encoding is performed so that each gene is composed of the elements listed by ti identified in (1). The crossover operator is defined similarly to the multi-junction used in other problems such as TSP. The operator is defined as follows: • Select a partial route • Exchange of direct segments of tasks where ID matches  • The exchanges define a series of matches that relates each of the genes of a chromosome with which occupies the same position in the other parent Mutation operators define various modes that will be executed randomly, and just those mutations that improve the aptitude of chromosomes will be selected. The defined mutation operators are: exchange order of tasks, exchange of assigning contiguous tasks and changing the allocation of a task. Elitism operator is defined to keep the percentage of efficient solutions in every generation of population and population size as a constant this involves the re-placement of parents by the children chromosomes in generations with the excep-tion to remain with elite chromosomes. The roulette selection is the criteria chosen for this. The initiation of chromosomes is based on the received tasks; each chromo-some is initiated with the tasks of the new case. For each task of the chromosome a sequential search is made in the case and assigns the task to an existing agent so that this association is maintained for the remaining tasks. 3   Results and Conclusions The previous planning model of ALZ-MAS calculated the distribution of tasks us-ing neural networks to establish the final sequence of actions. This model was im-plemented by means of a RPTW (Routing Problems with Time Windows) neural network[4],which allows reaching a solution much more rapidly. In order to compare both systems, we measure the number of replannings car-ried out by the planner agent. The first planner of ALZ-MAS was applied in a Dynamic Distribution of Tasks in Health-Care Scenarios 57 working day and the process was repeated the next day using this new proposed planner. This process was repeated during 5 iterations. The image 1 shows the numbers of replannings carried out for each of the 5 nurses. The y axis represents the number of replannings and the x axis the nurses. The thin lines represent the RPTWplanner of ALZ-MAS and the fat lines the new planner. The colour repre-sents the nurses. The number of replannings is reduced because the new planner creates the plans bearing in mind the intervals among the tasks in the final plan.  Fig. 1 Numbers of replannings carried out for each of the 5 nurses. Fat lines new planer and thin lines RPTW. To complete the study the Mann-Whitney U-test [9] was applied. It was a non-parametric test and it is not necessary to make assumptions on the data distribu-tion, as in the t-test. The test determines two values: H0 and H1. H0 shows if the data in both groups presents the same distribution, whereas H1 determines if there is difference in number of replannings. The number of replannings for each worker was compared in both planners. The results establish that the number of replannings comes from different statistical distributions for each workerso, we can conclude that the differences are relevant. The proposed planner allows reducing the number of replannings because it es-tablishes homogeneous intervals among the tasks and calculates the arrival time of tasks using the previous information. Besides, the planner makes predictions of the necessary personal in order to accomplish all the activities. Acknowledgments. This study has been supported by the Spanish Ministry of Science and Innovation project TRA2009_0096. References 1. Camarinha-Matos, L., Afsarmanesh, H.: Design of a virtual community infrastructure for elderly care. In: PRO-VE 2002 – 3rd IFIP Working Conference on Infrastructures for Virtual Enterprises, Sesimbra, Portugal (2006) 58 C. Zato, A. de Luis, and J.F. De Paz 2. Zülch, G., Rottinger, S., Vollstedt, T.: A simulation approach for planning and reas-signing of personnel in manufacturing. International Journal of Production Econom-ics 90, 265–277 (2004) 3. Foster, D., McGregor, C., El-Masri, S.: A Survey of Agent-Based Intelligent Decision Support Systems to Support Clinical Management and Research. In: Proceedings of the First International Workshop on Multi-Agent Systems for Medicine, Computation-al Biology and Bioinformatics, pp. 16–35. Utretch, Netherlands (2005) 4. Bajo, J., De Paz, J.F., De Paz, Y., Corchado, J.M.: Integrating case-based planning and RPTW neural networks to construct an intelligent environment for health care. Journal Expert Systems Applications 36(3), 5844–5858 (2009) 5. Martín, Q., Santos, M.T., de Paz, Y.: Investigacion Operativa: Problemas y Ejercicios. Pearson Educacion, London (2005) 6. Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993) 7. Corchado, J., Tapia, D., Bravo, J., García, Ó., Saavedra, A., Alonso, R., García, I.: ALZ-MAS 2.0; A Distributed Approach for Alzheimer Health Care. In: 3rd Sympo-sium of Ubiquitous Computing and Ambient Intelligence 2008. AISC, vol. 51, pp. 76–85. Springer, Heidelberg (2008) 8. Martín, Q.: Investigación Operativa. Prentice-Hall, Englewood Cliffs (2003) 9. Yue, S., Wang, C.: The influence of serial correlation on the Mann–Whitney test for detecting a shift in median. Advances in Water Resources 25(3), 325–333 (2002) 10. Yu, J., Buyya, R.: Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Scientific Programming 14(3), 217–230 (2006) 11. Hougardy, S.: The Floyd–Warshall algorithm on graphs with negative cycles. Informa-tion Processing Letters 110(8-9), 279–281 (2010)  

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